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Add perfect freeze: lock prompt on Perfect rating, thaw on change signals

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When the previous run was rated Perfect and none of the thaw conditions
are met, the refiner skips all prompt transformations (alignment, repair,
wildcard cleanup, emphasis) and re-uses the previously encoded prompt
directly.

Thaw conditions (any one releases the freeze):
- feedback_prompt is provided (user explicitly wants a change)
- intent prompt changed significantly (body similarity < 0.50)
- source image fingerprint changed from previous run

The advisor analysis pass is also skipped on Perfect ratings with no
feedback - nothing to analyse when the output was already perfect, and
the repair would be blocked by the existing is_perfect guard anyway.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
digital-garbage
2026-05-16 00:01:50 +03:00
co-authored by Claude Sonnet 4.6
parent bbeb68d34f
commit bd2046962c
1 changed files with 31 additions and 3 deletions
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conditioning.py
+31 -3
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@@ -9077,10 +9077,11 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
if mode in {"Only prompt", "Full"} and not repair_signal and not is_perfect and not has_feedback:
return current_prompt, "Advisor: skipped; no repair signal and no user feedback.", "", False, ""
# Pass 1: analysis — runs for Full and Only diagnostics, but skipped when the user
# already provided explicit feedback (they told us what's wrong; no need to analyse).
# Pass 1: analysis — runs for Full and Only diagnostics, but skipped when:
# - user provided explicit feedback (they already said what's wrong), or
# - rating is Perfect with no feedback (nothing to analyse; repair will be blocked anyway).
analysis = ""
if mode in {"Full", "Only diagnostics"} and not has_feedback:
if mode in {"Full", "Only diagnostics"} and not has_feedback and not is_perfect:
analysis_prompt_text = self._v2_advisor_analysis_prompt(
current_prompt,
intent_prompt,
@@ -10063,6 +10064,24 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
pre_advisor_prompt = ""
feedback_history = ""
advisor_rating_label = rating_label
is_perfect_rating = has_previous_run and learning_profile.get("key") == "like"
prev_intent_key = self._v2_prompt_key(
(previous_run or {}).get("intent_source_prompt", "") or (previous_run or {}).get("intent_prompt", "")
)
intent_drifted = bool(
prev_intent_key and intent_source_prompt and
self._v2_prompt_body_similarity(prev_intent_key, intent_source_prompt) < 0.50
)
image_changed = bool(isinstance(vision_context, dict) and vision_context.get("changed_from_previous", False))
perfect_freeze = (
is_perfect_rating
and not str(feedback_prompt or "").strip()
and not intent_drifted
and not image_changed
and not learning_mode
and not current_prompt_refusal
)
if current_prompt_refusal:
prompt_to_encode = analysis_prompt
lucky_status = "Prompt refusal filter: enhancer refusal detected; current prompt will not be stored or learned."
@@ -10076,6 +10095,15 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
repair_status = "Prompt repair: skipped in Learning mode."
wildcard_status = "Scene Builder wildcard cleanup: skipped in Learning mode."
encoded_role = "learning passthrough"
elif perfect_freeze:
frozen = self._v2_prompt_key(previous_run.get("encoded_prompt", "")) or analysis_prompt
prompt_to_encode = frozen
lucky_status = "Perfect freeze: prompt locked to previous perfect output."
intent_alignment_status = "Intent alignment: skipped (perfect freeze)."
perfect_repair_status = "Perfect repairs: skipped (perfect freeze)."
repair_status = "Prompt repair: skipped (perfect freeze)."
wildcard_status = "Scene Builder wildcard cleanup: skipped (perfect freeze)."
encoded_role = "perfect frozen prompt"
elif im_feeling_lucky and clip is not None:
prompt_to_encode, lucky_status = self._v2_compose_lucky_prompt(
analysis_prompt,